ResumeBias

Blind Web App

Disrupt the District 2018

Demo

http://www.webapp3-dev4.us-east-1.elasticbeanstalk.com

Purpose

To support equality and diversity in the hiring process.

Why

White male applicants recieve up to 36% more callbacks than their peers based on their names.

What

Blind strips the name off of the resume to reduce racial or gender bias during the first round of hiring.

How

Blind uses natural laguage processing to identify the name and email to generate a new resume.

Usage

Upload your resume in PDF format on the demo website. Choose how much information you want to remove by clicking the buttons below the submission box. The new PDF will automatically download onto your computer.

Dependencies

Python 3.6+

nltk (Stanford NLP module optional)

numpy

pdfminer.six

PyPDF2

unidecode

Sources Used:

NLTK

Bird, Steven, Edward Loper and Ewan Klein (2009), Natural Language Processing with Python. O'Reilly Media Inc. (nltk.org)

Email Regex

http://www.regular-expressions.info/email.html

Stanford NLP with NLTK

https://blog.manash.me/configuring-stanford-parser-and-stanford-ner-tagger-with-nltk-in-python-on-windows-f685483c374a

NFL Players Dataset

https://raw.githubusercontent.com/theliamcrawford/6-Degrees-of-NFL-Players/master/names.txt